# Split Happens: Combating Advanced Threats with Split Learning and Function Secret Sharing

arXiv: https://arxiv.org/abs/2507.10494 · HTML: https://mlsanity.com/paper/2507.10494/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 32 references that are in the map.
- First posted on arXiv 14 Jul 2025.

## Builds on (most cited first, 18 of 18)

- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Unleashing the Tiger: Inference Attacks on Split Learning](https://mlsanity.com/paper/2012.02670/) (2020)
- [FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning](https://mlsanity.com/paper/2004.02229/) (2020)
- [CryptoDL: Deep Neural Networks over Encrypted Data](https://mlsanity.com/paper/1711.05189/) (2017)
- [UnSplit: Data-Oblivious Model Inversion, Model Stealing, and Label Inference Attacks Against Split Learning](https://mlsanity.com/paper/2108.09033/) (2021)
- [Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?](https://mlsanity.com/paper/2003.12365/) (2020)
- [ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning](https://mlsanity.com/paper/2205.04007/) (2022)
- [POSEIDON: Privacy-Preserving Federated Neural Network Learning](https://mlsanity.com/paper/2009.00349/) (2020)
- [ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing](https://mlsanity.com/paper/2006.04593/) (2020)
- [A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack against Split Learning](https://mlsanity.com/paper/2405.04115/) (2024)
- [Split Ways: Privacy-Preserving Training of Encrypted Data Using Split Learning](https://mlsanity.com/paper/2301.08778/) (2023)
- [Love or Hate? Share or Split? Privacy-Preserving Training Using Split Learning and Homomorphic Encryption](https://mlsanity.com/paper/2309.10517/) (2023)
- [GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption](https://mlsanity.com/paper/2401.14840/) (2024)
- [Split Without a Leak: Reducing Privacy Leakage in Split Learning](https://mlsanity.com/paper/2308.15783/) (2023)
- [Learning in the Dark: Privacy-Preserving Machine Learning using Function Approximation](https://mlsanity.com/paper/2309.08190/) (2023)
- [Make Split, not Hijack: Preventing Feature-Space Hijacking Attacks in Split Learning](https://mlsanity.com/paper/2404.09265/) (2024)
- [A More Secure Split: Enhancing the Security of Privacy-Preserving Split Learning](https://mlsanity.com/paper/2309.08697/) (2023)
- [A Pervasive, Efficient and Private Future: Realizing Privacy-Preserving Machine Learning Through Hybrid Homomorphic Encryption](https://mlsanity.com/paper/2409.06422/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption](https://mlsanity.com/paper/2507.14853/) (2025)

## Abstract (arXiv)

Split Learning (SL) -- splits a model into two distinct parts to help protect client data while enhancing Machine Learning (ML) processes. Though promising, SL has proven vulnerable to different attacks, thus raising concerns about how effective it may be in terms of data privacy. Recent works have shown promising results for securing SL through the use of a novel paradigm, named Function Secret Sharing (FSS), in which servers obtain shares of a function they compute and operate on a public input hidden with a random mask. However, these works fall short in addressing the rising number of attacks which exist on SL. In SplitHappens, we expand the combination of FSS and SL to U-shaped SL. Similarly to other works, we are able to make use of the benefits of SL by reducing the communication and computational costs of FSS. However, a U-shaped SL provides a higher security guarantee than previous works, allowing a client to keep the labels of the training data secret, without having to share them with the server. Through this, we are able to generalize the security analysis of previous works and expand it to different attack vectors, such as modern model inversion attacks as well as label inference attacks. We tested our approach for two different convolutional neural networks on different datasets. These experiments show the effectiveness of our approach in reducing the training time as well as the communication costs when compared to simply using FSS while matching prior accuracy.